A Dynamic Routing Framework for Shared Mobility Services

TL;DR

This paper introduces a dynamic routing framework for shared mobility services that optimizes multi-passenger routes using a novel space window concept and an efficient Alternating Minimization algorithm, significantly improving computational efficiency.

Contribution

It presents a new dynamic routing framework with the space window concept and an Alternating Minimization algorithm for shared mobility, enhancing efficiency and reducing costs.

Findings

Order of magnitude improvement in computational efficiency

Minimal optimality gap compared to standard algorithms

Effective reduction in travel time for shuttle and passengers

Abstract

Travel time in urban centers is a significant contributor to the quality of living of its citizens. Mobility on Demand (MoD) services such as Uber and Lyft have revolutionized the transportation infrastructure, enabling new solutions for passengers. Shared MoD services have shown that a continuum of solutions can be provided between the traditional private transport for an individual and the public mass transit based transport, by making use of the underlying cyber-physical substrate that provides advanced, distributed, and networked computational and communicational support. In this paper, we propose a novel shared mobility service using a dynamic framework. This framework generates a dynamic route for multi-passenger transport, optimized to reduce time costs for both the shuttle and the passengers and is designed using a new concept of a space window. This concept introduces a degree of freedom that helps reduce the cost of the system involved in designing the optimal route. A specific algorithm based on the Alternating Minimization approach is proposed. Its analytical properties are characterized. Detailed computational experiments are carried out to demonstrate the advantages of the proposed approach and are shown to result in an order of magnitude improvement in the computational efficiency with minimal optimality gap when compared to a standard Mixed Integer Quadratically Constrained Programming based algorithm.